Siamese Network-Based System for Criminal Identification

EasyChair Preprint 8787

Abstract

To discourage criminal activities, the large number of CCTV installations throughout the country play a crucial role. Through this paper, we propose an AI-based solution that can leverage these devices to remotely identify and report absconding criminals. Using the one-shot learning approach, we present a face recognition algorithm that yields accurate results even with low training data. The Siamese Network architecture is used to verify if the face embeddings of the image detected is the same as that of the criminal. Two parallel neural networks are designed to take one input each- one being the detected face and the other being an embedding from the dataset. The outputs of the two networks are compared to predict whether the detected face is the same as the input face or not. This algorithm is further integrated with an automated model for updating the information of the recognized criminal into the database along with updating the appropriate law enforcement authorities about the last known whereabouts.

Keyphrases: computer vision, face recognition, machine learning

@booklet{EasyChair:8787,
  author    = {Soham Dave and Parth Kansara and Vinaya Sawant and Shivam Mehta},
  title     = {Siamese Network-Based System for Criminal Identification},
  howpublished = {EasyChair Preprint 8787},
  year      = {EasyChair, 2022}}